Cargando…

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out th...

Descripción completa

Detalles Bibliográficos
Autores principales: Scangos, Katherine Wilson, Khambhati, Ankit N., Daly, Patrick M., Owen, Lucy W., Manning, Jeremy R., Ambrose, Josiah B., Austin, Everett, Dawes, Heather E., Krystal, Andrew D., Chang, Edward F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566975/
https://www.ncbi.nlm.nih.gov/pubmed/34744662
http://dx.doi.org/10.3389/fnhum.2021.746499
_version_ 1784594133959049216
author Scangos, Katherine Wilson
Khambhati, Ankit N.
Daly, Patrick M.
Owen, Lucy W.
Manning, Jeremy R.
Ambrose, Josiah B.
Austin, Everett
Dawes, Heather E.
Krystal, Andrew D.
Chang, Edward F.
author_facet Scangos, Katherine Wilson
Khambhati, Ankit N.
Daly, Patrick M.
Owen, Lucy W.
Manning, Jeremy R.
Ambrose, Josiah B.
Austin, Everett
Dawes, Heather E.
Krystal, Andrew D.
Chang, Edward F.
author_sort Scangos, Katherine Wilson
collection PubMed
description Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.
format Online
Article
Text
id pubmed-8566975
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-85669752021-11-05 Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology Scangos, Katherine Wilson Khambhati, Ankit N. Daly, Patrick M. Owen, Lucy W. Manning, Jeremy R. Ambrose, Josiah B. Austin, Everett Dawes, Heather E. Krystal, Andrew D. Chang, Edward F. Front Hum Neurosci Neuroscience Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy. Frontiers Media S.A. 2021-10-21 /pmc/articles/PMC8566975/ /pubmed/34744662 http://dx.doi.org/10.3389/fnhum.2021.746499 Text en Copyright © 2021 Scangos, Khambhati, Daly, Owen, Manning, Ambrose, Austin, Dawes, Krystal and Chang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Scangos, Katherine Wilson
Khambhati, Ankit N.
Daly, Patrick M.
Owen, Lucy W.
Manning, Jeremy R.
Ambrose, Josiah B.
Austin, Everett
Dawes, Heather E.
Krystal, Andrew D.
Chang, Edward F.
Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology
title Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology
title_full Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology
title_fullStr Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology
title_full_unstemmed Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology
title_short Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology
title_sort distributed subnetworks of depression defined by direct intracranial neurophysiology
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566975/
https://www.ncbi.nlm.nih.gov/pubmed/34744662
http://dx.doi.org/10.3389/fnhum.2021.746499
work_keys_str_mv AT scangoskatherinewilson distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT khambhatiankitn distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT dalypatrickm distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT owenlucyw distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT manningjeremyr distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT ambrosejosiahb distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT austineverett distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT dawesheathere distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT krystalandrewd distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology
AT changedwardf distributedsubnetworksofdepressiondefinedbydirectintracranialneurophysiology